Is the distribution of state-action pairs from sample based planning accurate for small experience sets?
I am aware that as the experience set grows the Central Limit Theorem
  will come into play and the distribution of experience will more
  accurately represent the true environment's state-actions-rewards
  distribution


I believe here you mean the Law of Large Numbers which states that for a large enough sample ($n \rightarrow \infty$) the sample mean will converge to the true mean. The central limit theorem (CLT) states that if you take the sum/mean of a set of independent random variables then the distribution of this new RV will be approximately normal as $n \rightarrow \infty$. 


  before this happens is sampled based planning still useful


As you mentioned, if you had only 5 full episodes of experience to choose from, then this likely would not represent true underlying distributions and so the approximations would not be good -- of course, this will depend on how complex your MDP is, if you had a trivially simple one then it may be enough to represent it well. As David Silver says in his lecture, then one of the disadvantages of planning with a model is that you introduce another set of uncertainty, mainly from when you approximate the properties of the model.